Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations cover safety and idempotency. Description adds mechanics (calls ai_visibility_check, ranks results) but no new behavioral warnings. Adequately transparent but not exceptional.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with core function, then use case and output. No redundancy, every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, description provides output details (ranked list with score, confidence, signal density). Sufficient for agent to understand use and behavior. Minor gap: error handling or rate limits not mentioned.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. Description adds context beyond schema: first entity as subject, models options with api key requirement, context for disambiguation. Adds meaningful value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it compares AI visibility across multiple entities and ranks them, with a specific use case (competitive audits). It distinguishes from sibling ai_visibility_check (single entity) and compare_entities (generic comparison).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states usefulness for competitive AI-marketing audits and gives a concrete example question. Implies when to use versus single-entity check, but lacks explicit exclusion scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.